{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the basic libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the environment for running the experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paths for loading/storing results.\n",
    "name = 'results/HGaussian_MINE' # filename\n",
    "chkpt_name = name+'.pt'              # checkpoint\n",
    "fig_name = name+'.pdf'               # output figure\n",
    "\n",
    "# use GPU if available\n",
    "if torch.cuda.is_available(): \n",
    "    torch.set_default_tensor_type(torch.cuda.FloatTensor)\n",
    "else:\n",
    "    torch.set_default_tensor_type(torch.FloatTensor)\n",
    "\n",
    "# initialize random seed\n",
    "np.random.seed(0)\n",
    "torch.manual_seed(0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate data using the gaussian model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_size = 400   # sample size\n",
    "rho = 0.9           # model parameter\n",
    "p = 0.8             # parameter of bernoulli distribution\n",
    "mu = 1              # mean of Gaussian distribution\n",
    "rep = 1             # number of repeated runs\n",
    "d = 1               # number of dimensions for X (and Y)\n",
    "X = np.zeros((rep,sample_size,d))\n",
    "Y = np.zeros((rep,sample_size,d))\n",
    "Z = np.zeros((rep,sample_size,d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(rep):\n",
    "    Z[i] = np.random.normal(\n",
    "            loc=mu,\n",
    "            scale=rho,\n",
    "            size=sample_size).reshape(sample_size,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate data from bernoulli distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import bernoulli\n",
    "for i in range(rep):\n",
    "    X_ = bernoulli.rvs(p, size=sample_size)\n",
    "    X_[np.where(X_==0)] = -1\n",
    "    X[i] = X_.reshape(sample_size, d)\n",
    "\n",
    "# data_Y is the product of data from bernoulli distribution and mixed Gaussian distribution.\n",
    "for i in range(rep):\n",
    "    for j in range(sample_size):\n",
    "        Y[i,j] = X[i,j] * Z[i,j]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A plot of the first dimension of $Y$ against that of $X$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[0,:,0],Y[0,:,0],label=\"data\",marker=\"+\",color=\"steelblue\")\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title('Plot of data samples')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the MINE model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model.mine import MINE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 100       # batch size of data sample\n",
    "lr = 1e-4              # learning rate\n",
    "ma_rate = 0.1          # rate of moving average in the gradient estimate \n",
    "\n",
    "mine_list = []\n",
    "for i in range(rep):\n",
    "    mine_list.append(MINE(torch.Tensor(X[i]),torch.Tensor(Y[i]),\n",
    "                            batch_size=batch_size,lr=lr,ma_rate=ma_rate))\n",
    "dXY_list = np.zeros((rep,0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load previous results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_available = True # set to False to prevent loading previous results\n",
    "if load_available and os.path.exists(chkpt_name):\n",
    "    checkpoint = torch.load(\n",
    "        chkpt_name, map_location='cuda' if torch.cuda.is_available() else 'cpu')\n",
    "    dXY_list = checkpoint['dXY_list']\n",
    "    mine_state_list = checkpoint['mine_state_list']\n",
    "    for i in range(rep):\n",
    "        mine_list[i].load_state_dict(mine_state_list[i])\n",
    "    print('Previous results loaded.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Continuously train the model. The following can be executed repeatedly and after loading previous results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-99108d92c873>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m                 \u001b[0mmine_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m                 \u001b[0mdXY_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmine_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m         \u001b[0;31m# To show intermediate works\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/github/MI-NEE/model/mine.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, X, Y)\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m             \u001b[0mXY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m         \u001b[0mX_ref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_resample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    114\u001b[0m         \u001b[0mY_ref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_resample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0mXY_ref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_ref\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_ref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/github/MI-NEE/model/mine.py\u001b[0m in \u001b[0;36m_resample\u001b[0;34m(data, batch_size, replace)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;31m# Resample the given data sample.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     index = np.random.choice(\n\u001b[0;32m---> 11\u001b[0;31m         range(data.shape[0]), size=batch_size, replace=replace)\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/torch/lib/python3.7/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mprod\u001b[0;34m(a, axis, dtype, out, keepdims, initial)\u001b[0m\n\u001b[1;32m   2662\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2663\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2664\u001b[0;31m \u001b[0;34m@\u001b[0m\u001b[0marray_function_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prod_dispatcher\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2665\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mprod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NoValue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NoValue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2666\u001b[0m     \"\"\"\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "continue_train = True  # set to True to continue to train\n",
    "num_big_steps = 10     # number of small steps\n",
    "num_small_steps = 300  # number of big steps\n",
    "if continue_train:\n",
    "    for k in range(num_big_steps):\n",
    "        for j in range(num_small_steps):\n",
    "            dXY_list = np.append(dXY_list, np.zeros((rep, 1)), axis=1)\n",
    "            for i in range(rep):\n",
    "                mine_list[i].step()\n",
    "                dXY_list[i, -1] = mine_list[i].forward()\n",
    "        # To show intermediate works\n",
    "        for i in range(rep):\n",
    "            plt.plot(dXY_list[i, :],label='dXY')\n",
    "            plt.title('Plots of divergence estimates')\n",
    "        display.clear_output(wait=True)\n",
    "        display.display(plt.gcf())\n",
    "    display.clear_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save current results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current results saved.\n"
     ]
    }
   ],
   "source": [
    "overwrite = False  # set to True to overwrite previously stored results\n",
    "if overwrite or not os.path.exists(chkpt_name):\n",
    "    mine_state_list = [mine_list[i].state_dict() for i in range(rep)]\n",
    "    torch.save({\n",
    "        'dXY_list': dXY_list,\n",
    "        'mine_state_list': mine_state_list\n",
    "    }, chkpt_name)\n",
    "    print('Current results saved.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the ground truth mutual information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground truth is 4.982193620464953 nats.\n"
     ]
    }
   ],
   "source": [
    "mi = g.ground_truth * d\n",
    "print('Ground truth is {} nats.'.format(mi))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply moving average to smooth out the mutual information estimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mi_ma_rate = 0.01            # rate of moving average\n",
    "mi_list = dXY_list.copy()    # see also the estimate() member function of MINE\n",
    "for i in range(1,dXY_list.shape[1]):\n",
    "    mi_list[:,i] = (1-mi_ma_rate) * mi_list[:,i-1] + mi_ma_rate * mi_list[:,i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the mutual information estimate after different number of iterations. The red dashed line shows the ground truth, and the green dotted line is the number of iterations where 90% of the ground truth is reached."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   "source": [
    "plt.axhline(mi,label='ground truth',linestyle='--',color='red')\n",
    "for i in range(rep):\n",
    "    plt.plot(mi_list[i,:],color='steelblue')\n",
    "    for t in range(mi_list[i].shape[0]):\n",
    "        if (mi_list[0,t]>.6*mi):\n",
    "            plt.axvline(t,label='60% reached',linestyle=':',color='green')\n",
    "            break\n",
    "plt.xlim((0,mi_list[0].shape[0]))\n",
    "plt.ylim((0,mi*1.1))\n",
    "plt.xlabel(\"number of iterations\")\n",
    "plt.ylabel(\"MI estimate\")\n",
    "plt.legend()\n",
    "plt.savefig(fig_name)"
   ]
  }
 ],
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